Stable, online learning using CMACs for neuroadaptive tracking control of flexible-joint manipulators

Abstract

An artificial neural network is proposed for the precision control of flexible-joint robots. The training method uses backstepping in an online, direct neuroadaptive scheme in order to guarantee stability. The online weight updates include a learning term that improves performance while maintaining stability. Albus's cerebellar model arithmetic computer algorithm is modified to work for flexible robots by utilizing radial basis functions to deal with the elasticity. The resulting hybrid network is referred to as CMAC-RBF associative memory or CRAM network. Many of the properties of the CMAC for rigid robot control are kept by using CRAM for flexible-joint robots.